We Audited 200+ Local Businesses — Here's What the Top 10% Do Differently

We've audited 200+ businesses. Average score: 19/100. But a small group — about 10% — scores above 50. We wanted to know why.

Over the past five months, we've run structured AI visibility audits on 214 local businesses across 12 industries — from dental practices and law firms to landscapers, fitness studios, and real estate agencies. Every business was tested against 25 real queries across ChatGPT, Gemini, Claude, and Perplexity. That's over 20,000 individual AI responses analyzed.

The score distribution tells a stark story.

Score Distribution Across 214 Businesses

AEO Score Range	% of Businesses	Count
0–10	35%	75
11–25	38%	81
26–50	20%	43
51–75	6%	13
76–100	<1%	2

Nearly three-quarters of local businesses are functionally invisible to AI. They don't get mentioned, don't get recommended, and don't appear in any AI-generated answer about their category in their city.

But that top 7% — the 15 businesses scoring above 50 — share five specific traits. None of these are accidental.

Pattern 1: Structured Data on the Website

100% of businesses scoring above 50 had JSON-LD schema markup. Only 14% of businesses scoring below 25 had any schema at all.

The top performers didn't just have basic Organization schema. They had layered markup: LocalBusiness with geo-coordinates, FAQPage schema on their FAQ sections, Service schema for individual offerings, and Review schema pulling in aggregated ratings.

One dental practice in Austin had 11 distinct schema types across their site. Their AEO Score: 67. A competing practice two miles away — similar reviews, similar SEO rankings — had zero schema. Their score: 18.

Structured data is the single clearest differentiator between businesses AI can parse and businesses AI ignores. It gives AI engines a machine-readable map of your business: what you do, where you are, what people think of you, and what questions you answer.

Pattern 2: Authoritative Third-Party Citations

Businesses scoring above 50 appeared in an average of 8.3 authoritative third-party sources. Businesses below 25 averaged 1.7 — almost entirely limited to Yelp and Google Business.

The top scorers weren't just listed in directories. They were cited in local news articles, featured in industry publications, included in expert roundup posts, referenced in university or government databases, and mentioned in niche community forums.

A personal injury law firm in Chicago scoring 61 had been quoted in three local news outlets, was cited in a state bar association resource page, and appeared in two legal industry comparison articles. AI engines pulled from all of these sources when generating answers about personal injury lawyers in Chicago.

A competing firm with more Google reviews and a higher ad spend scored 22. Their only external presence was a Yelp listing and an Avvo profile.

The distinction matters because AI engines triangulate trust. A single source — even a great website — is not enough. AI needs corroboration from multiple independent, authoritative sources before it will confidently recommend a business.

Pattern 3: Deep, Specific Content

Top-scoring businesses had an average of 47 indexed pages with substantive content. Bottom-scoring businesses averaged 8 pages, most of them thin service descriptions.

This isn't about word count. It's about content that answers real questions in detail.

A physiotherapy clinic in Denver scoring 54 had a 22-article blog covering specific conditions (frozen shoulder rehab timelines, post-ACL surgery milestones, sciatica vs. piriformis syndrome), a 35-question FAQ section organized by condition, and four detailed case studies with anonymized patient outcomes.

When someone asks Perplexity "best physiotherapy for frozen shoulder recovery in Denver," that clinic has a page that directly answers the question with specific detail. AI engines cite it because there's something worth citing.

Compare that to the typical competitor: a single "Services" page listing "Physical Therapy, Sports Rehab, Post-Surgical Recovery" with one paragraph each. There's nothing for AI to grab onto. No specifics, no expertise signal, no citable content.

The pattern held across every industry we audited. Businesses with detailed FAQ sections scored 19 points higher on average. Businesses with published case studies scored 16 points higher. Businesses with educational blog content covering specific, long-tail topics scored 14 points higher.

Pattern 4: Consistent Entity Data Across Platforms

Top scorers had 92% NAP (Name, Address, Phone) consistency across platforms. Bottom scorers averaged 61%.

This one is invisible to most business owners, but AI engines notice it immediately. When your business name is "Smith & Associates Legal Group" on your website, "Smith Associates" on Google Business, "Smith and Associates Legal" on Yelp, and "Smith Legal Group" on your Facebook page — AI engines aren't sure these are the same entity.

Entity confusion dilutes authority. AI can't aggregate signals across sources if it can't confidently match them to one business.

A boutique real estate agency in Scottsdale scoring 58 had the exact same business name, address format, phone number, and business description across 14 platforms. AI engines had no ambiguity about who they were, what they did, and where they operated.

We found that for every 10% drop in NAP consistency, AEO Scores dropped by an average of 7 points. Businesses with consistency below 70% almost never scored above 30, regardless of their other signals.

Pattern 5: Visible Expertise Signals

83% of businesses scoring above 50 displayed clear expertise markers on their websites. Only 19% of businesses below 25 did.

Expertise signals include: named author bios with credentials, professional certifications and affiliations displayed on service pages, team pages with individual specializations listed, and association memberships or accreditations shown prominently.

A financial advisor in Portland scoring 56 had a detailed bio listing their CFP certification, 18 years of experience, a published column in a regional business journal, and membership in the Financial Planning Association. Their team page listed each advisor's specializations and credentials.

A competing advisor with similar experience but a generic "About Us" page with no names, no credentials, and a stock photo scored 14.

AI engines are explicitly trained to evaluate expertise, especially for YMYL (Your Money, Your Life) queries. When someone asks "best financial advisor in Portland for retirement planning," AI looks for evidence that a business has actual expertise — not just a claim.

What the Bottom 70% Have in Common

The 156 businesses scoring below 25 shared their own set of patterns — and they're the mirror image of the top performers:

• No structured data. 86% had zero schema markup of any kind.
• Generic websites. Template designs with thin, undifferentiated service pages. Average of 6 pages total.
• No third-party authority. External presence limited to basic directory listings, most of them incomplete or inconsistent.
• Inconsistent entity data. Business name, address, or phone number varied across platforms in 39% of cases.
• No expertise signals. No author names, no credentials, no professional affiliations visible on the site.

These businesses often had decent Google rankings and strong review profiles. In the traditional search world, they were doing fine. But AI engines evaluate businesses through a completely different lens — and by those standards, these businesses are invisible.

The Compounding Effect

What makes this data particularly striking is that the five patterns compound. Businesses with just one of these traits scored marginally better than average. Businesses with three or more scored dramatically better.

Traits Present (of 5)	Average AEO Score
0	8
1	16
2	27
3	41
4	54
5	72

No single fix transforms a business's AI visibility. But stacking structured data, authoritative citations, deep content, entity consistency, and expertise signals creates a compounding effect that AI engines respond to decisively.

What You Can Do With This

These five patterns aren't abstract. They're specific, measurable, and implementable. A business can add schema markup in a day. NAP consistency can be fixed in a week. FAQ content can be published in a month. Third-party citations take longer, but even one or two quality placements move the needle.

The current landscape is wide open. When 73% of local businesses score below 25 and the average is 19, reaching 50 puts you in the top 7% of your market. That's not a high bar — it's an uncontested opportunity.

Methodology

• Sample: 214 local businesses across 12 industries (dental, legal, fitness, real estate, home services, financial advisory, veterinary, wellness/spa, architecture, physiotherapy, automotive, hospitality)
• Geography: US and European markets, 47 cities
• Queries: 25 per business across ChatGPT (GPT-4), Gemini 2.5, Claude 3.5, Perplexity
• Scoring: Based on mention rate, recommendation position, sentiment, citation quality, and source attribution
• Period: November 2025 – March 2026

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Want to know where your business falls in this distribution? Check your AI visibility score — we'll audit your brand across all major AI engines and show you exactly how you compare.